Caltech101
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Image Classification | Caltech101 Base and New Classes | Base Accuracy98.95 | 50 | |
| Multi-view Clustering | Caltech101 20 | ACC76.81 | 30 | |
| Fine-grained classification | Caltech101 (base classes) | Accuracy98.23 | 27 | |
| Fine-grained classification | Caltech101 (novel classes) | MCE0.39 | 15 | |
| Few-shot Image Classification | Caltech101 (test) | Accuracy (1-shot)94.17 | 15 | |
| Image Classification | N-Caltech101 (test) | Accuracy81.73 | 13 | |
| Fine-grained classification | Caltech101 fine-grained (base) | MCE0.24 | 12 | |
| Image Classification | Caltech101 New classes | Accuracy94.93 | 12 | |
| Image Classification | Caltech101 Base classes | Accuracy99.23 | 12 | |
| Clustering | CALTECH101-7 | AMI0.6592 | 9 | |
| Image Classification | Caltech101 Pathological Non-IID (test) | Accuracy97.02 | 9 | |
| Event-based Classification | N-Caltech101 (test) | GFLOPs0.7 | 9 | |
| Classification | Caltech101 20 | Accuracy92.48 | 7 | |
| Clustering | Caltech101 20 (test) | Accuracy45.12 | 7 | |
| Image Classification | Caltech101 (unseen) | Accuracy94 | 4 | |
| Image Classification | Caltech101 (Novel) | Top-1 Acc94.5 | 4 | |
| Image Classification | Caltech101 (PGD-l∞ attack, ε=4/255) (test) | Robust Acc80.73 | 4 | |
| Image Classification | Caltech101 PGD-l2 attack, ε=0.5 (test) | Robust Accuracy89.21 | 4 | |
| Clustering | Caltech101 7 (test) | Accuracy88.27 | 2 | |
| Unseen prompt generalization | Caltech101 | Accuracy94.93 | 2 |
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